Research on Mechanical Fault Diagnosis and Prediction Technology Based on Deep Learning

Abstract

This study introduces an innovative deep learning architecture, amalgamating Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, termed the CNN-LSTM model. Its efficacy in both identifying and anticipating mechanical failures is explored through an examination of vibration datasets sourced from actual industrial machinery. The assessment delves into the model's capabilities across various fault categories and severities. Findings indicate that the CNN-LSTM model exhibits remarkable precision in fault identification, with forecasted outcomes largely aligning with actual fault occurrences, thus corroborating its diagnostic efficacy. A comparative analysis against traditional diagnostic techniques further elucidates the superior performance of the proposed model, as evidenced by its enhanced accuracy, recall, and F1 score metrics. Such results underscore the deep learning model's advanced precision and dependability when addressing intricate fault prediction tasks. This study proves the superior performance of CNN-LSTM model in the task of mechanical fault diagnosis and prediction. This discovery provides strong evidence for the application of deep learning in industrial field, and provides new tools and methods for solving practical engineering problems.